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2021年5月21日,《IEEE Transactions on Multimedia》正式刊发了国地质大学计算机学院与国防科技大学、深圳大学以及国家超算济南中心等单位合作完成的研究成果—Consensus Graph Learning for Multi-view Clustering(基于一致图学习的多视图聚类算法),论文的第一作者为中国地质大学计算机学院硕士研究生李正来,通讯作者为唐厂教授。
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After s...
The current global image descriptors are mostly obtained by using the local image features aggregation, which fail to take full account of the details of the image, resulting in the loss of the semant...
Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented ...
An international team of researchers, including Carnegie Mellon University’s Rachel Mandelbaum, has shown that the relationship between galaxy clusters and their surrounding dark matter halo is more c...
For a long time, gravitational instability in the disk of planetesimals has been suspected to be the main engine responsible for the beginning of dust growth, its advantage being that it provides forr...
The aim of this work is to investigate the clustering properties of the large earthquakes which occurred in Italy in the last four centuries.
Random instances of Constraint Satisfaction Problems (CSP’s) appear to be hard for all known algorithms, when the number of constraints per variable lies in a certain interval. Contributing to the gen...
The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A ...
Clustering with mixtures of log-concave distributions     EM algorithm  Log-concave distribution  Clustering  Normal copula       font style='font-size:12px;'> 2015/8/20
The EM algorithm is a popular tool for clustering observations via a parametric mixture model. Two disadvantages of this approach are that its success depends on the appropriateness of the assumed pa...
This paper presents a new application of a data-clustering algorithm in Landsat image classification, which improves on conventional classification methods. Neural networks have been widely used in ...
This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning a...
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired...
We measure the filling factor, correlation function, and power spectrum of transmitted flux in a large sample of Lyα forest spectra, comprised of 30 Keck HIRES spectra and 23 Keck LRIS spectra. We inf...
The clustering dipole of the local universe from the Two Micron All Sky Survey     galaxies  clusters       font style='font-size:12px;'> 2014/12/24
The unprecedented sky coverage and photometric uniformity of the Two Micron All Sky Survey (2MASS) provides a rich resource for investigating the galaxies populating the local universe. A full charact...

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